Chapter 2. Getting Started

In this chapter, we cover what you need to know to begin building and modifying machine learning applications on low-power devices. All the software is free, and the hardware development kits are available for less than $30, so the biggest challenge is likely to be the unfamiliarity of the development environment. To help with that, throughout the chapter we recommend a well-lit path of tools that we’ve found work well together.

Who Is This Book Aimed At?

To build a TinyML project, you will need to know a bit about both machine learning and embedded software development. Neither of these are common skills, and very few people are experts on both, so this book will start with the assumption that you have no background in either of these. The only requirements are that you have some familiarity running commands in the terminal (or Command Prompt on Windows), and are able to load a program source file into an editor, make alterations, and save it. Even if that sounds daunting, we walk you through everything we discuss step by step, like a good recipe, including screenshots (and screencasts online) in many cases, so we’re hoping to make this as accessible as possible to a wide audience.

We’ll show you some practical applications of machine learning on embedded devices, using projects like simple speech recognition, detecting gestures with a motion sensor, and detecting people with a camera sensor. We want to get you comfortable with building these programs ...

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